DravidianMultiModality: A Dataset for Multi-modal Sentiment Analysis in Tamil and Malayalam
National University of Ireland Galway（爱尔兰国立高威大学）；Amrita Vishwa Vidyapeetham（阿姆里塔大学）；Indian Institute of Information Technology and Management-Kerala（喀拉拉邦印度信息技术与管理学院）；Universiti Pendidikan Sultan Idris（苏丹依德理斯教育大学）
Bharathi Raja Chakravarthi, Jishnu Parameswaran P. K, Premjith B, K. P Soman, Rahul Ponnusamy, Prasanna Kumar Kumaresan, Kingston Pal Thamburaj, John P. McCrae
Human communication is inherently multimodal and asynchronous. Analyzing human emotions and sentiment is an emerging field of artificial intelligence. We are witnessing an increasing amount of multimodal content in local languages on social media about products and other topics. However, there are not many multimodal resources available for under-resourced Dravidian languages. Our study aims to create a multimodal sentiment analysis dataset for the under-resourced Tamil and Malayalam languages. First, we downloaded product or movies review videos from YouTube for Tamil and Malayalam. Next, we created captions for the videos with the help of annotators. Then we labelled the videos for sentiment, and verified the inter-annotator agreement using Fleiss's Kappa. This is the first multimodal sentiment analysis dataset for Tamil and Malayalam by volunteer annotators.
Findings of the Sentiment Analysis of Dravidian Languages in Code-Mixed Text
National University of Ireland Galway（爱尔兰国立高威大学）；ULTRA Arts and Science College（ULTRA文理学院）；Eastern University, Sri Lanka（斯里兰卡东方大学）；Sri Sivasubramaniya Nadar College of Engineering（斯里西瓦苏布拉马尼亚·纳达尔工程学院）；Indian Institute of Information Technology and Management-Kerala（印度喀拉拉邦信息技术与管理学院）；Indian Institute of Information Technology Tiruchirappalli（印度蒂鲁吉拉伯利信息技术学院）；University of Moratuwa, Sri Lanka（斯里兰卡莫拉图瓦大学）
Advancing Sentiment Analysis in Tamil-English Code-Mixed Texts: Challenges and Transformer-Based Solutions
Instituto Politécnico Nacional (IPN)（墨西哥国立理工学院）；Centro de Investigación en Computación (CIC)（计算机研究中心）；Escuela Superior de Ingeniería Mecánica y Eléctrica (ESIME)（机械与电气工程高等学院）
Theedhum Nandrum@Dravidian-CodeMix-FIRE2020: A Sentiment Polarity Classifier for YouTube Comments with Code-switching between Tamil, Malayalam and English
DataWeave；Exotel
Theedhum Nandrum是一个使用两种方法的情感极性检测系统-基于随机梯度下降（SGD）的分类器和基于长短期记忆（LSTM）的分类器。我们的方法利用了语言功能，如使用表情符号，选择脚本和代码混合，这些功能在为Dravidian Codemix - FIRE 2020任务指定的数据集中非常明显。SGD的超参数使用GridSearchCV进行调整。我们的系统在泰米尔语英语中排名第四，加权平均F1得分为0.62，在马拉雅拉姆语英语中排名第九，得分为0.65。在任务截止日期后，我们使用基于Logistic回归的模型获得了泰米尔语英语的加权平均F1分数为0.77。这种性能大大优于该数据集上排名最高的分类器。我们使用特定语言的Soundex来协调代码混合数据中的拼写变体，这似乎是Soundex的一个新应用。我们的完整代码发布在github网站https://github.com/oligoglot/theedhum-nandrum上。
DravidianCodeMix: Sentiment Analysis and Offensive Language Identification Dataset for Dravidian Languages in Code-Mixed Text
National University of Ireland Galway（爱尔兰国立大学戈尔韦分校）；ULTRA Arts and Science College（ULTRA艺术与科学学院）；Cardiff University（卡迪夫大学）；Indian Institute of Information Technology and Management-Kerala（印度喀拉拉邦信息技术与管理学院）
A Sentiment Analysis Dataset for Code-Mixed Malayalam-English
Insight SFI Research Centre for Data Analytics（Insight SFI数据分析研究中心）；National University of Ireland Galway（爱尔兰国立大学戈尔韦分校）；Indian Institute of Information Technology and Management-Kerala（喀拉拉邦信息技术与管理学院）
Offensive Language Identification in Low-resourced Code-mixed Dravidian languages using Pseudo-labeling
Indian Institute of Information Technology Tiruchirappalli（蒂鲁奇拉帕利印度信息技术学院）；ULTRA Arts and Science College（ULTRA文理学院）；Eastern University（东方大学）；Kongu Engineering College（孔古工程学院）；Sri Sivasubramaniya Nadar College of Engineering（斯里·西瓦苏布拉马尼亚·纳达尔工程学院）；Insight SFI Research Centre for Data Analytics, National University of Ireland Galway（爱尔兰国立大学戈尔韦分校SFI洞察数据分析研究中心）
KBCNMUJAL@HASOC-Dravidian-CodeMix-FIRE2020: Using Machine Learning for Detection of Hate Speech and Offensive Code-Mixed Social Media text
Institute of Management and Research, Jalgaon, Affil- KBC North Maharashtra University（贾尔冈管理与研究学院，隶属于KBC北马哈拉施特拉大学）；School of Computer Sciences, KBC North Maharashtra University（KBC北马哈拉施特拉大学计算机科学学院）；Copenhagen Business School（哥本哈根商学院）；BMCC college of commerce, Pune（BMCC商学院，浦那）
BHARATI: Morphology-Aware Tokenizers for Classical Indian Languages with Subword Fertility Analysis
Centre for Sensors, Instrumentation and Cyber-Physical System Engineering (Centre for SeNSE), Indian Institute of Technology Delhi（印度理工学院德里分校传感器、仪器仪表和网络物理系统工程中心（SeNSE中心））；RSL Quantum, FITT, IIT Delhi（印度理工学院德里分校FITT的RSL量子公司）
Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM Network
Fraunhofer FIT（弗劳恩霍夫应用信息技术研究所）；RWTH Aachen University（亚琛工业大学）；Insight SFI Research Centre for Data Analytics（Insight SFI数据分析研究中心）；Data Science Institute（数据科学研究所）；National University of Ireland Galway（爱尔兰国立大学戈尔韦分校）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）
L3Cube-IndicHeadline-ID: A Dataset for Headline Identification and Semantic Evaluation in Low-Resource Indian Languages
Department of Information Technology, PICT, Pune（信息科技系，PICT，普那）；Indian Institute of Technology Madras, Chennai（印度理工学院马德拉斯分校，钦奈）；L3Cube Labs, Pune（L3Cube实验室，普那）
IKS-Instruct: A 24,000-Example Multilingual Dataset for Teaching Language Models Indian Knowledge Systems
Intrinsic Lab, Centre for Sensors, Instrumentation and Cyber-Physical System Engineering (Centre for SeNSE), Indian Institute of Technology Delhi（印度理工学院德里分校传感器、仪器仪表和网络物理系统工程中心（传感中心）本征实验室）；RSL Quantum, FITT, IIT Delhi（印度理工学院德里分校 FITT 学院 RSL 量子实验室）
